{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6b299ee9",
   "metadata": {},
   "source": [
    "# Recursive multi-step forecasting\n",
    "\n",
    "Recursive multi-step forecasting involves using the predicted values from previous time steps as input to forecast the values for the subsequent time steps. The model initially predicts one time step ahead and then uses that forecast as an input for the next time step, continuing this recursive process until the desired forecast horizon is reached.\n",
    "\n",
    "To clarify, since the value of $t_{n-1}$ is required to predict $t_{n}$, and $t_{n-1}$ is unknown, a recursive process is applied in which, each new prediction is based on the previous one.\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/diagram-recursive-mutistep-forecasting.png\" style=\"width: 500px\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Diagram of recursive multi-step forecasting</i></font>\n",
    "</p>\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/recursive-forecasting-gif.gif\" style=\"width: 500px; padding: 10px; background-color: white; border-radius: 4px;\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Recursive forecasting</i></font>\n",
    "</p>\n",
    "\n",
    "When using machine learning models for recursive multi-step forecasting, one of the major challenges is transforming the time series data into a matrix format that relates each value of the series to the preceding time window (lags). This process can be complex and time-consuming, requiring careful feature engineering to ensure the model can accurately capture the underlying patterns in the data.\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/matrix_transformation_with_exog_variable.png\" style=\"width: 500px;\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Time series transformation including an exogenous variable</i></font>\n",
    "</p>\n",
    "\n",
    "By using the classes <code>ForecasterRecursive</code>, it is possible to build machine learning models for recursive multi-step forecasting with ease. These classes can automatically transform the time series data into a matrix format suitable for input to a machine learning algorithm, and provide a range of options for tuning the model parameters to achieve the best possible performance."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d1ae70e1-1354-4aa6-9dc4-73d3c7b016c5",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5c54bcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from skforecast.datasets import fetch_dataset\n",
    "from skforecast.preprocessing import RollingFeatures\n",
    "from skforecast.recursive import ForecasterRecursive\n",
    "from skforecast.plot import plot_prediction_intervals, set_dark_theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c0024f6-5d9b-4e0d-a332-dfb71280d7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭────────────────────────────────────── <span style=\"font-weight: bold\">h2o</span> ───────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health    │\n",
       "│ system had between 1991 and 2008.                                                │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd        │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman │\n",
       "│ /fpp3package, http://OTexts.com/fpp3.                                            │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/h2o.csv                                                       │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 204 rows x 2 columns                                                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭────────────────────────────────────── \u001b[1mh2o\u001b[0m ───────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health    │\n",
       "│ system had between 1991 and 2008.                                                │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd        │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman │\n",
       "│ /fpp3package, http://OTexts.com/fpp3.                                            │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/h2o.csv                                                       │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mShape:\u001b[0m 204 rows x 2 columns                                                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Download data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(\n",
    "    name=\"h2o\", raw=True, kwargs_read_csv={\"names\": [\"y\", \"datetime\"], \"header\": 0}\n",
    ")\n",
    "\n",
    "# Data preprocessing\n",
    "# ==============================================================================\n",
    "data['datetime'] = pd.to_datetime(data['datetime'], format='%Y-%m-%d')\n",
    "data = data.set_index('datetime')\n",
    "data = data.asfreq('MS')\n",
    "data = data['y']\n",
    "data = data.sort_index()\n",
    "\n",
    "# Split train-test\n",
    "# ==============================================================================\n",
    "steps = 36\n",
    "data_train = data[:-steps]\n",
    "data_test  = data[-steps:]\n",
    "\n",
    "# Plot\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "data_train.plot(ax=ax, label='train')\n",
    "data_test.plot(ax=ax, label='test')\n",
    "ax.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9801a209-b601-4d9b-aa3c-a2e03df98c98",
   "metadata": {},
   "source": [
    "## Create and train forecaster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "349d1b95-5b16-4620-a46e-d595b4187ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            #margin: auto;\n",
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       "        }\n",
       "        .container-66a743511b0e47dcac16d0d3f07775d1 summary:hover {\n",
       "            color: #000000;\n",
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       "        .container-66a743511b0e47dcac16d0d3f07775d1 ul {\n",
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       "        .container-66a743511b0e47dcac16d0d3f07775d1 li strong {\n",
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       "        .container-66a743511b0e47dcac16d0d3f07775d1 a {\n",
       "            color: #001633;\n",
       "            text-decoration: none;\n",
       "        }\n",
       "        .container-66a743511b0e47dcac16d0d3f07775d1 a:hover {\n",
       "            color: #359ccb; \n",
       "        }\n",
       "    </style>\n",
       "    \n",
       "        <div class=\"container-66a743511b0e47dcac16d0d3f07775d1\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterRecursive</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> LGBMRegressor</li>\n",
       "                    <li><strong>Lags:</strong> [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]</li>\n",
       "                    <li><strong>Window features:</strong> ['roll_mean_10']</li>\n",
       "                    <li><strong>Window size:</strong> 15</li>\n",
       "                    <li><strong>Series name:</strong> y</li>\n",
       "                    <li><strong>Exogenous included:</strong> False</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Differentiation order:</strong> None</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 14:38:10</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 14:38:12</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    None\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <MonthBegin></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterrecursive.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/autoregressive-forecaster.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "=================== \n",
       "ForecasterRecursive \n",
       "=================== \n",
       "Estimator: LGBMRegressor \n",
       "Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15] \n",
       "Window features: ['roll_mean_10'] \n",
       "Window size: 15 \n",
       "Series name: y \n",
       "Exogenous included: False \n",
       "Exogenous names: None \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Differentiation order: None \n",
       "Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <MonthBegin> \n",
       "Estimator parameters: \n",
       "    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0,\n",
       "    'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1,\n",
       "    'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0,\n",
       "    'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None,\n",
       "    'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0,\n",
       "    'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 14:38:10 \n",
       "Last fit date: 2025-11-26 14:38:12 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=10),\n",
    "                 transformer_y   = None, \n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data_train, store_in_sample_residuals=True)\n",
    "forecaster"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "733cef42-c7bf-42f8-8c3d-8471638599a4",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1bdf4f8-b999-4d97-899c-fbcc2af7066a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005-07-01    1.026507\n",
       "2005-08-01    1.042429\n",
       "2005-09-01    1.116730\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(steps=36)\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1b53f4b-be13-4e7b-bfdd-2cf0f3492452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "data_train.plot(ax=ax, label='train')\n",
    "data_test.plot(ax=ax, label='test')\n",
    "predictions.plot(ax=ax, label='predictions')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92bce057-008e-462a-9c46-4557013783bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test error (mse): 0.006632513357651682\n"
     ]
    }
   ],
   "source": [
    "# Prediction error\n",
    "# ==============================================================================\n",
    "error_mse = mean_squared_error(\n",
    "                y_true = data_test,\n",
    "                y_pred = predictions\n",
    "            )\n",
    "print(f\"Test error (mse): {error_mse}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "326a2c49",
   "metadata": {},
   "source": [
    "## Prediction intervals\n",
    "\n",
    "A prediction interval defines the interval within which the true value of `y` is expected to be found with a given probability. For example, the prediction interval (5, 95) can be expected to contain the true prediction value with 90% probability. Skforecast implemets several methods to estimate prediction intervals, for a detailed explanation of the different methods visit [Probabilistic forecasting](../user_guides/probabilistic-forecasting-overview.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cab13451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-07-01</th>\n",
       "      <td>1.026507</td>\n",
       "      <td>0.992734</td>\n",
       "      <td>1.104815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-08-01</th>\n",
       "      <td>1.042429</td>\n",
       "      <td>1.021760</td>\n",
       "      <td>1.120737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-09-01</th>\n",
       "      <td>1.116730</td>\n",
       "      <td>1.061465</td>\n",
       "      <td>1.196657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                pred  lower_bound  upper_bound\n",
       "2005-07-01  1.026507     0.992734     1.104815\n",
       "2005-08-01  1.042429     1.021760     1.120737\n",
       "2005-09-01  1.116730     1.061465     1.196657"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict intervals\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict_interval(\n",
    "                    steps    = 36,\n",
    "                    interval = [5, 95],\n",
    "                    method   = 'bootstrapping',\n",
    "                    n_boot   = 100\n",
    "             )\n",
    "predictions.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af40db29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot prediction interval\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "plot_prediction_intervals(\n",
    "    predictions         = predictions,\n",
    "    y_true              = data_test,\n",
    "    target_variable     = 'y',\n",
    "    title               = \"Prediction intervals\",\n",
    "    kwargs_fill_between = {'color': 'gray', 'alpha': 0.3, 'zorder': 1},\n",
    "    ax                  = ax\n",
    ")\n",
    "ax.legend(loc='upper left');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dbe3c4dc-4b71-42f0-a4c9-d6815535272d",
   "metadata": {},
   "source": [
    "## Feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8394cca9-e86d-4309-b10c-ea26485f85e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>lag_12</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lag_11</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lag_2</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lag_1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>lag_13</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>roll_mean_10</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lag_9</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lag_7</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lag_6</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>lag_14</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lag_3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>lag_15</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lag_5</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lag_10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lag_4</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lag_8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature  importance\n",
       "11        lag_12          88\n",
       "10        lag_11          47\n",
       "1          lag_2          45\n",
       "0          lag_1          35\n",
       "12        lag_13          35\n",
       "15  roll_mean_10          30\n",
       "8          lag_9          29\n",
       "6          lag_7          28\n",
       "5          lag_6          26\n",
       "13        lag_14          24\n",
       "2          lag_3          23\n",
       "14        lag_15          20\n",
       "4          lag_5          19\n",
       "9         lag_10          19\n",
       "3          lag_4          17\n",
       "7          lag_8          15"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecaster.get_feature_importances()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "aa1a1be6",
   "metadata": {},
   "source": [
    "## Training and prediction matrices\n",
    "\n",
    "While the primary goal of building forecasting models is to predict future values, it is equally important to evaluate if the model is effectively learning from the training data. Analyzing predictions on the training data or exploring the prediction matrices is crucial for assessing model performance and understanding areas for optimization. This process can help identify issues like overfitting or underfitting, as well as provide deeper insights into the model’s decision-making process. Check the [How to Extract Training and Prediction Matrices](../user_guides/training-and-prediction-matrices.html) user guide for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec40fb95",
   "metadata": {},
   "source": [
    "## Differentiation\n",
    "\n",
    "Time series differentiation involves computing the differences between consecutive observations in the time series. When it comes to training forecasting models, differentiation offers the advantage of focusing on relative rates of change rather than directly attempting to model the absolute values. Once the predictions have been estimated, this transformation can be easily reversed to restore the values to their original scale."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "904ef57b",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "To learn more about modeling time series differentiation, visit our example: <a href=\"https://www.cienciadedatos.net/documentos/py49-modelling-time-series-trend-with-tree-based-models.html\">Modelling time series trend with tree based models</a>.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e57d5074",
   "metadata": {},
   "outputs": [
    {
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       "    \n",
       "        <div class=\"container-19eafc4f2e1d4721bc8608c425ecf4bc\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterRecursive</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> LGBMRegressor</li>\n",
       "                    <li><strong>Lags:</strong> [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]</li>\n",
       "                    <li><strong>Window features:</strong> None</li>\n",
       "                    <li><strong>Window size:</strong> 16</li>\n",
       "                    <li><strong>Series name:</strong> y</li>\n",
       "                    <li><strong>Exogenous included:</strong> False</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Differentiation order:</strong> 1</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 14:38:16</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 14:38:16</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    None\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <MonthBegin></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterrecursive.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/autoregressive-forecaster.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "=================== \n",
       "ForecasterRecursive \n",
       "=================== \n",
       "Estimator: LGBMRegressor \n",
       "Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15] \n",
       "Window features: None \n",
       "Window size: 16 \n",
       "Series name: y \n",
       "Exogenous included: False \n",
       "Exogenous names: None \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Differentiation order: 1 \n",
       "Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <MonthBegin> \n",
       "Estimator parameters: \n",
       "    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0,\n",
       "    'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1,\n",
       "    'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0,\n",
       "    'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None,\n",
       "    'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0,\n",
       "    'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 14:38:16 \n",
       "Last fit date: 2025-11-26 14:38:16 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags            = 15,\n",
    "                 differentiation = 1\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data_train)\n",
    "forecaster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c6e34b0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005-07-01    0.909529\n",
       "2005-08-01    0.941633\n",
       "2005-09-01    1.007650\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(steps=36)\n",
    "predictions.head(3)"
   ]
  }
 ],
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